Alternatives to Codacy

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

As per my work experience and knowledge, I have chosen the followings stacks to this mission.

UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

Happy Coding! Suggestions are welcome! :)

Thanks, Ganesa

The continuous integration process for our Rails backend app starts by opening a GitHub pull request. This triggers a CircleCI build and some Code Climate checks.

The CircleCI build is a workflow that runs the following jobs:

• check for security vulnerabilities with Brakeman
• check code quality with RuboCop
• run RSpec tests in parallel with the knapsack gem, and output test coverage reports with the simplecov gem
• upload test coverage to Code Climate

Code Climate checks the following:

• code quality metrics like code complexity
• test coverage minimum thresholds

The CircleCI jobs and Code Climate checks above have corresponding GitHub status checks.

Once all the mandatory GitHub checks pass and the code+functionality have been reviewed, developers can merge their pull request into our Git master branch. Code is then ready to deploy!

#ContinuousIntegration

We use Codecov because it's a lot better than Coveralls. Both of them provide the useful feature of having nice web-accessible reports of which files have what level of test coverage (though every coverage tool produces reasonably nice HTML in a directory on the local filesystem), and can report on PRs cases where significant new code was added without test coverage.

That said, I'm pretty unhappy with both of them for our use case. The fundamental problem with both of them is that they don't handle the ~1% probability situations with missing data due to networking flakiness well. The reason I think our use case is relevant is that we submit coverage data from multiple jobs (one that runs our frontend test suite and another that runs our backend test suite), and the coverage provider is responsible for combining that data together.

I think the problem is if a test suite runs successfully but due to some operational/networking error between Travis/CircleCI and Codecov the coverage data for part of the codebase doesn't get submitted, Codecov will report a huge coverage drop in a way that is very confusing for our contributors (because they experience it as "why did the coverage drop 12%, all I did was added a test").

We migrated from Coveralls to Codecov because empirically this sort of breakage happened 10x less on Codecov, but it still happens way more often than I'd like.

I wish they put more effort in their retry mechanism and/or providing clearer debugging information (E.g. a big "Missing data" banner) so that one didn't need to be specifically told to ignore Codecov/Coveralls when it reports a giant coverage drop.

Codecov Although I actually use both codecov and Coveralls, I very much like the graphs I get from codecov, and some of their diagnostic tools.

We use Codecov because it's a lot better than Coveralls. Both of them provide the useful feature of having nice web-accessible reports of which files have what level of test coverage (though every coverage tool produces reasonably nice HTML in a directory on the local filesystem), and can report on PRs cases where significant new code was added without test coverage.

That said, I'm pretty unhappy with both of them for our use case. The fundamental problem with both of them is that they don't handle the ~1% probability situations with missing data due to networking flakiness well. The reason I think our use case is relevant is that we submit coverage data from multiple jobs (one that runs our frontend test suite and another that runs our backend test suite), and the coverage provider is responsible for combining that data together.

I think the problem is if a test suite runs successfully but due to some operational/networking error between Travis/CircleCI and Codecov the coverage data for part of the codebase doesn't get submitted, Codecov will report a huge coverage drop in a way that is very confusing for our contributors (because they experience it as "why did the coverage drop 12%, all I did was added a test").

We migrated from Coveralls to Codecov because empirically this sort of breakage happened 10x less on Codecov, but it still happens way more often than I'd like.

I wish they put more effort in their retry mechanism and/or providing clearer debugging information (E.g. a big "Missing data" banner) so that one didn't need to be specifically told to ignore Codecov/Coveralls when it reports a giant coverage drop.

Codecov Although I actually use both codecov and Coveralls, I very much like the graphs I get from codecov, and some of their diagnostic tools.

It is very important to have clean code. To be sure that the code quality is not really bad I use a few tools. I love SonarQube with many relevant hints and deep analysis of code. codebeat isn't so detailed, but it can find complexity issues and duplications. Codacy cannot find more bugs then your IDE. The winner for me is SonarQube that shows me really relevant bugs in my code.

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

Our whole Vue.js frontend stack (incl. SSR) consists of the following tools:

• Nuxt.js consisting of Vue CLI, Vue Router, vuex, Webpack and Sass (Bundler for HTML5, CSS 3), Babel (Transpiler for JavaScript),
• Vue Styleguidist as our style guide and pool of developed Vue.js components
• Vuetify as Material Component Framework (for fast app development)
• TypeScript as programming language
• Apollo / GraphQL (incl. GraphiQL) for data access layer (https://apollo.vuejs.org/)
• ESLint, TSLint and Prettier for coding style and code analyzes
• Jest as testing framework
• Google Fonts and Font Awesome for typography and icon toolkit
• NativeScript-Vue for mobile development

The main reason we have chosen Vue.js over React and AngularJS is related to the following artifacts:

• Empowered HTML. Vue.js has many similar approaches with Angular. This helps to optimize HTML blocks handling with the use of different components.
• Detailed documentation. Vue.js has very good documentation which can fasten learning curve for developers.
• Adaptability. It provides a rapid switching period from other frameworks. It has similarities with Angular and React in terms of design and architecture.
• Awesome integration. Vue.js can be used for both building single-page applications and more difficult web interfaces of apps. Smaller interactive parts can be easily integrated into the existing infrastructure with no negative effect on the entire system.
• Large scaling. Vue.js can help to develop pretty large reusable templates.
• Tiny size. Vue.js weights around 20KB keeping its speed and flexibility. It allows reaching much better performance in comparison to other frameworks.

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

Our whole Vue.js frontend stack (incl. SSR) consists of the following tools:

• Nuxt.js consisting of Vue CLI, Vue Router, vuex, Webpack and Sass (Bundler for HTML5, CSS 3), Babel (Transpiler for JavaScript),
• Vue Styleguidist as our style guide and pool of developed Vue.js components
• Vuetify as Material Component Framework (for fast app development)
• TypeScript as programming language
• Apollo / GraphQL (incl. GraphiQL) for data access layer (https://apollo.vuejs.org/)
• ESLint, TSLint and Prettier for coding style and code analyzes
• Jest as testing framework
• Google Fonts and Font Awesome for typography and icon toolkit
• NativeScript-Vue for mobile development

The main reason we have chosen Vue.js over React and AngularJS is related to the following artifacts:

• Empowered HTML. Vue.js has many similar approaches with Angular. This helps to optimize HTML blocks handling with the use of different components.
• Detailed documentation. Vue.js has very good documentation which can fasten learning curve for developers.
• Adaptability. It provides a rapid switching period from other frameworks. It has similarities with Angular and React in terms of design and architecture.
• Awesome integration. Vue.js can be used for both building single-page applications and more difficult web interfaces of apps. Smaller interactive parts can be easily integrated into the existing infrastructure with no negative effect on the entire system.
• Large scaling. Vue.js can help to develop pretty large reusable templates.
• Tiny size. Vue.js weights around 20KB keeping its speed and flexibility. It allows reaching much better performance in comparison to other frameworks.